Often, the information bandwidth to be transmitted is greater than the available bandwidth. Therefore, information is often compressed before it is transmitted (or stored), to reduce the required bandwidth. For example, the HDTV standard was defined, at its inception, to include compression. Many types of signals are compressed, including still images, video and telephone conversations. The reigning compression standards are JPEG for still images and MPEG (I, II, III or IV) for video. In actuality, these standards are standards for the compressed signals. There is no particular requirements, in the standard, on the method for converting the uncompressed signals into compressed signals.
Compression and in some cases decompression are often very demanding and typically require dedicated hardware. Both JPEG and MPEG are transform-based methods, in which the uncompressed data is transformed into a transform space, where the data is represented by a set of coefficients. It is usually desirable that the coefficients have less autocorrelation than the image data or even no autocorrelation at all. Although the DCT transform does not completely decorrelate the coefficients, the correlation between them is significantly reduced. In other compression methods, other transform spaces are used. In transform space, some of the coefficients have a greater visual and/or other effect on the image, than other coefficients. To obtain compression, the coefficients are quantized, with fewer bits being allocated to those coefficients which have a lesser effect. Typically, a coefficient is quantized by dividing it by a weight and then rounding or truncating the result.
Optical and electro-optical processors have been used in the art, to a small extent, for computationally demanding applications. However, with the advent of very fast electronic computer components and parallel processors, their acceptance has been limited.
Performing some types of linear transforms, for example Fourier transforms, continuous cosine transforms and Walash transforms, using optical components is well known, for example, as described in “Cosinusoidal Transforms in White Light”, by N. George and S. Wang, in Applied Optics, Vol. 23, No. 6, Mar. 15, 1984, in “Hartley Transforms for Hybrid Pattern Matching”, by Nomura, K. Itoh and Y. Ichioka, in Applied Optics, Vol. 29, No. 29, Oct., 10, 1990, in “Lens Design for a White-Light Cosine-Transform Achromat”, by K. B. Farr and S. Wang, in Applied Optics, Vol. 34, No. 1, Jan. 1, 1995 and in “Optical Computing”, by D. Feitelson, in a chapter titled “Optical Image and Signal Processing”, pp. 102–104 (which pages describe general discrete linear transforms using a lenslet array), and pp. 117–129 (which describe matrix multiplication), MIT Press 1988, the disclosures of which are incorporated herein by reference.